0 Bibliographic Collection

Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)

Data format: *.bib

Query: “big data” (All Fields) and “national park” (All Fields)

Timespan: 2015-2024

Document Type: Articles, letters, review and proceedings papers

Query data: May, 2024

# Loading data
myfile <- ("data_raw/search_1.bib")

# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(file=myfile, dbsource="isi",format="bibtex")
## 
## Converting your isi collection into a bibliographic dataframe
## 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

1 Descriptive Analysis

Although bibliometrics is mainly known for quantifying the scientific production and measuring its quality and impact, it is also useful for displaying and analyzing the intellectual, conceptual and social structures of research as well as their evolution and dynamical aspects.

In this way, bibliometrics aims to describe how specific disciplines, scientific domains, or research fields are structured and how they evolve over time. In other words, bibliometric methods help to map the science (so-called science mapping) and are very useful in the case of research synthesis, especially for the systematic ones.

Bibliometrics is an academic science founded on a set of statistical methods, which can be used to analyze scientific big data quantitatively and their evolution over time and discover information. Network structure is often used to model the interaction among authors, papers/documents/articles, references, keywords, etc.

Bibliometrix is an open-source software for automating the stages of data-analysis and data-visualization. After converting and uploading bibliographic data in R, Bibliometrix performs a descriptive analysis and different research-structure analysis.

Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.

1.1 Main findings

#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)


MAIN INFORMATION ABOUT DATA

 Timespan                              2015 : 2024 
 Sources (Journals, Books, etc)        63 
 Documents                             82 
 Annual Growth Rate %                  22.03 
 Document Average Age                  3.04 
 Average citations per doc             23.6 
 Average citations per year per doc    4.194 
 References                            5155 
 
DOCUMENT TYPES                     
 article                             71 
 article; early access               3 
 article; retracted publication      1 
 editorial material                  1 
 proceedings paper                   4 
 review                              2 
 
DOCUMENT CONTENTS
 Keywords Plus (ID)                    385 
 Author's Keywords (DE)                432 
 
AUTHORS
 Authors                               625 
 Author Appearances                    671 
 Authors of single-authored docs       1 
 
AUTHORS COLLABORATION
 Single-authored docs                  1 
 Documents per Author                  0.131 
 Co-Authors per Doc                    8.18 
 International co-authorships %        32.93 
 

Annual Scientific Production

 Year    Articles
    2015        1
    2016        4
    2017        4
    2018        4
    2019        7
    2020        9
    2021       13
    2022       12
    2023       22
    2024        6

Annual Percentage Growth Rate 22.03 


Most Productive Authors

   Authors        Articles Authors        Articles Fractionalized
1       HE H             4   CIESIELSKI M                   1.000
2       ZHENG H          4   ZOU SS                         1.000
3       DELANG CO        3   LI W                           0.833
4       HEURICH M        3   LI Y                           0.667
5       LI               3   ZHANG X                        0.643
6       LI X             3   HE H                           0.601
7       LI Y             3   ZHENG H                        0.601
8       LU J             3   CHAPMAN CA                     0.500
9       WU Y             3   CHEN J                         0.500
10      ZHANG X          3   HALPENNY E                     0.500


Top manuscripts per citations

                             Paper                                    DOI  TC TCperYear   NTC
1  MCKINLEY DC, 2017, BIOL CONSERV          10.1016/j.biocon.2016.05.015  583     72.88 2.740
2  HEIKINHEIMO V, 2017, ISPRS INT GEO-INF   10.3390/ijgi6030085           178     22.25 0.837
3  SWANSON A, 2016, CONSERV BIOL            10.1111/cobi.12695            157     17.44 3.323
4  RICH LN, 2017, GLOB ECOL BIOGEOGR        10.1111/geb.12600              88     11.00 0.414
5  MANCINI F, 2018, PLOS ONE                10.1371/journal.pone.0200565   69      9.86 2.173
6  GATTI RC, 2022, PROC NATL ACAD SCI U S A 10.1073/pnas.2115329119        66     22.00 5.462
7  SHASHA ZT, 2020, ENVIRON SCI POLLUT RES  10.1007/s11356-020-08584-9     54     10.80 1.984
8  LI X, 2019, SCI BULL                     10.1016/j.scib.2019.07.004     53      8.83 3.373
9  LASTOVICKA J, 2020, REMOTE SENS          10.3390/rs12121914             47      9.40 1.727
10 BARROS C, 2020, CURR ISSUES TOUR         10.1080/13683500.2019.1619674  47      9.40 1.727


Corresponding Author's Countries

          Country Articles   Freq SCP MCP MCP_Ratio
1  CHINA                32 0.3902  26   6     0.188
2  USA                  20 0.2439  14   6     0.300
3  GERMANY               4 0.0488   1   3     0.750
4  UNITED KINGDOM        4 0.0488   2   2     0.500
5  SPAIN                 3 0.0366   3   0     0.000
6  AUSTRALIA             2 0.0244   1   1     0.500
7  CANADA                2 0.0244   1   1     0.500
8  FINLAND               2 0.0244   1   1     0.500
9  ITALY                 2 0.0244   0   2     1.000
10 JAPAN                 2 0.0244   0   2     1.000


SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

     Country      Total Citations Average Article Citations
1  USA                        953                     47.65
2  UNITED KINGDOM             250                     62.50
3  CHINA                      245                      7.66
4  FINLAND                    185                     92.50
5  GERMANY                     57                     14.25
6  SPAIN                       51                     17.00
7  CZECH REPUBLIC              47                     47.00
8  CANADA                      38                     19.00
9  KOREA                       32                     32.00
10 SWITZERLAND                 22                     22.00


Most Relevant Sources

                                                      Sources        Articles
1  SUSTAINABILITY                                                           6
2  ECOLOGICAL INDICATORS                                                    3
3  FORESTS                                                                  3
4  GLOBAL ECOLOGY AND CONSERVATION                                          3
5  REMOTE SENSING                                                           3
6  CONSERVATION BIOLOGY                                                     2
7  CURRENT ISSUES IN TOURISM                                                2
8  HELIYON                                                                  2
9  INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH        2
10 JOURNAL OF DESTINATION MARKETING \\& MANAGEMENT                          2


Most Relevant Keywords

   Author Keywords (DE)      Articles Keywords-Plus (ID)     Articles
1    BIG DATA                      11          NATIONAL-PARK       13
2    NATIONAL PARK                  6          PATTERNS            11
3    PROTECTED AREA                 5          BIG DATA             9
4    ECOSYSTEM SERVICES             4          CONSERVATION         8
5    MACHINE LEARNING               4          IMPACT               7
6    NATIONAL PARKS                 4          BIODIVERSITY         6
7    TIME SERIES                    4          MANAGEMENT           6
8    TOURISM                        4          TOURISM              6
9    ARTIFICIAL INTELLIGENCE        3          FRAMEWORK            5
10   CLIMATE CHANGE                 3          VISITATION           5
plot(x=results, k=10, pause=F)

Warning: Removed 1 rows containing non-finite values (`stat_align()`).

1.2 Most Cited References

CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
                                                                                                      [,1]
WOOD SA, 2013, SCI REP-UK, V3, DOI 10.1038/SREP02976                                                     8
HAUSMANN A, 2018, CONSERV LETT, V11, DOI 10.1111/CONL.12343                                              7
HEIKINHEIMO V, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/IJGI6030085                                    7
SESSIONS C, 2016, J ENVIRON MANAGE, V183, P703, DOI 10.1016/J.JENVMAN.2016.09.018                        7
TENKANEN H, 2017, SCI REP-UK, V7, DOI 10.1038/S41598-017-18007-4                                         7
BALMFORD A, 2009, PLOS BIOL, V7, DOI 10.1371/JOURNAL.PBIO.1000144                                        6
BLEI DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/JMLR.2003.3.4-5.993                               6
BALMFORD A, 2015, PLOS BIOL, V13, DOI 10.1371/JOURNAL.PBIO.1002074                                       5
GUO Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/J.TOURMAN.2016.09.009                                5
HEIKINHEIMO V, 2020, LANDSCAPE URBAN PLAN, V201, DOI 10.1016/J.LANDURBPLAN.2020.103845                   4
KEELER BL, 2015, FRONT ECOL ENVIRON, V13, P76, DOI 10.1890/140124                                        4
PARACCHINI ML, 2014, ECOL INDIC, V45, P371, DOI 10.1016/J.ECOLIND.2014.04.018                            4
SONTER LJ, 2016, PLOS ONE, V11, DOI 10.1371/JOURNAL.PONE.0162372                                         4
STEFFAN-DEWENTER I., 2002, ECOLOGY, V83, P1421, DOI 10.1890/0012-9658(2002)0831421:SDEOLC2.0.CO          4
TOIVONEN T, 2019, BIOL CONSERV, V233, P298, DOI 10.1016/J.BIOCON.2019.01.023                             4
WATSON JEM, 2014, NATURE, V515, P67, DOI 10.1038/NATURE13947                                             4
ZHU Z, 2014, REMOTE SENS ENVIRON, V144, P152, DOI 10.1016/J.RSE.2014.01.011                              4
BROWN G, 2011, LANDSCAPE URBAN PLAN, V102, P1, DOI 10.1016/J.LANDURBPLAN.2011.03.003                     3
BURTON AC, 2015, J APPL ECOL, V52, P675, DOI 10.1111/1365-2664.12432                                     3
CESSFORD GORDON, 2003, JOURNAL FOR NATURE CONSERVATION (JENA), V11, P240, DOI 10.1078/1617-1381-00055    3

2 The Intellectual Structure of the field

Citation analysis is one of the main classic techniques in bibliometrics. It shows the structure of a specific field through the linkages between nodes (e.g. authors, papers, or journals), while the edges can be differently interpreted depending on the network type, that are namely co-citation, direct citation, bibliographic coupling. Please see Aria, Cuccurullo (2017).

Below there are three examples.

First, a co-citation network that shows relations between cited-reference works (nodes).

Second, a co-citation network that uses cited-journals as unit of analysis.

The useful dimensions to comment the co-citation networks are: (i) centrality and peripherality of nodes, (ii) their proximity and distance, (iii) strength of ties, (iv) clusters, (iiv) bridging contributions.

Third, a historiograph is built on direct citations. It draws the intellectual linkages in a historical order. Cited works of thousands of authors contained in a collection of published scientific articles is sufficient for recostructing the historiographic structure of the field, calling out the basic works in it.

2.1 Article (reference) co-citation analysis

Plot options:

  • n = 50 (the funxtion plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 1 (defines the size of vertex labels)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • edges.min = 5 (plots only edges with a strength greater than or equal to 5)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)

Descriptive analysis of Article co-citation network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  5123 
 Density                               0.017 
 Transitivity                          0.928 
 Diameter                              9 
 Degree Centralization                 0.08 
 Average path length                   3.955 
 

Journal (source) co-citation analysis

M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=10, remove.multiple=FALSE, labelsize=0.8,edgesize = 3, edges.min=5)

Descriptive analysis of Journal co-citation network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  2328 
 Density                               0.037 
 Transitivity                          0.397 
 Diameter                              4 
 Degree Centralization                 0.456 
 Average path length                   2.169 
 

3 Historiograph - Direct citation linkages

histResults <- histNetwork(M, sep = ";")
## 
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
## 
## Analyzing 5428 reference items...
## 
## Found 4 documents with no empty Local Citations (LCS)
options(width = 130)
net <- histPlot(histResults, n=20, size = 5, labelsize = 4)


 Legend

                                                                    Label
1              MANCINI F, 2018, PLOS ONE DOI 10.1371/JOURNAL.PONE.0200565
2          CHUN J, 2020, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2020.104136
3      BARROS C, 2020, CURR ISSUES TOUR DOI 10.1080/13683500.2019.1619674
4 CIESIELSKI M, 2021, FOREST POLICY ECON DOI 10.1016/J.FORPOL.2021.102509
5           LU J, 2023, TOUR MANAG PERSPECT DOI 10.1016/J.TMP.2023.101143
6              CIESIELSKI M, 2023, J MT SCI DOI 10.1007/S11629-023-8914-3
7           NYELELE C, 2023, ECOL INDIC DOI 10.1016/J.ECOLIND.2023.110638
8           MIZUUCHI Y, 2024, J FOR RES DOI 10.1080/13416979.2023.2257456
                                                                                                   Author_Keywords
1                                                                                                             <NA>
2                       NATURE-BASED TOURISM; TOURISM PRESSURE; PROTECTED AREA; SOCIAL BIG DATA; MANAGEMENT POLICY
3 SOCIAL MEDIA DATA; GEOTAGGED PHOTOGRAPHS; GPS TRACKS; NATURE-BASED; TOURISM; NATIONAL PARKS; VISITORS' BEHAVIOUR
4               FOREST RECREATIONAL FUNCTION; FLICKR; PUBLIC PREFERENCES; SOCIAL MEDIA; GEOTAGGED PHOTOS; BIG DATA
5                                    NATIONAL PARKS; MOBILE DEVICE LOCATION DATA; SOCIAL INEQUITY; DISTANCE; DECAY
6                                              ECOSYSTEM SERVICES; BIG DATA; TRAFFIC RESEARCH; MONITORING; FORESTS
7                                           LAKE TAHOE; VALUATION; ECOSYSTEM SERVICES; FLICKR; FOREST; TRAVEL COST
8           LANDSCAPE PERCEPTION; LANDSCAPE PREFERENCE; NATIONAL PARK; GIS; GEOTAGGED VISITOR EMPLOYED PHOTOGRAPHY
                                                                                                                                                                           KeywordsPlus
1                                                                               CULTURAL ECOSYSTEM SERVICES; TOURISM; DEMAND; ECOTOURISM; DESTINATION; VISITATION; LANDSCAPES; PATTERNS
2                                                                                 PUBLIC-PARTICIPATION GIS; HOME-RANGE; BIODIVERSITY; VALUES; MEDIA; VISITATION; LANDSCAPE; AREAS; LAND
3                             CULTURAL ECOSYSTEM SERVICES; GEOGRAPHIC INFORMATION; MOVEMENT PATTERNS; PROTECTED AREAS; BIG DATA; TOURISM; VISITATION; RECREATION; ECOTOURISM; FRAMEWORK
4 CULTURAL ECOSYSTEM SERVICES; GEOGRAPHIC INFORMATION DATA; SOCIAL MEDIA; DATA; PUBLIC PREFERENCES; NATIONAL-PARK; GEOTAGGED PHOTOGRAPHS; DATA-COLLECTION; CHOICE; RECREATION; PATTERNS
5                                                                                                              REGRESSION-MODELS; TRANSPORTATION; ACCESSIBILITY; TOURISM; IMPACT; RATES
6                                                              OUTDOOR RECREATION; NATIONAL-PARK; GREEN SPACE; URBAN; PARTICIPATION; PERCEPTIONS; MANAGEMENT; VISITORS; HOTSPOTS; AREAS
7                                     CULTURAL ECOSYSTEM SERVICES; NATIONAL-PARK VISITATION; OPPORTUNITY COST; VALUING NATURE; DEMAND; VALUATION; VISITORS; TIME; PHOTOGRAPHS; WILDFIRE
8                                             AGRARIAN LANDSCAPES; VISUAL PREFERENCES; SPATIAL-ANALYSIS; SOCIAL MEDIA; PERCEPTION; GIS; PHOTOGRAPHS; DIMENSIONS; PREDICTORS; ATTRIBUTES
                            DOI Year LCS GCS
1  10.1371/journal.pone.0200565 2018   1  69
2 10.1016/j.tourman.2020.104136 2020   1  32
3 10.1080/13683500.2019.1619674 2020   1  47
4  10.1016/j.forpol.2021.102509 2021   2  18
5     10.1016/j.tmp.2023.101143 2023   0   4
6     10.1007/s11629-023-8914-3 2023   0   0
7 10.1016/j.ecolind.2023.110638 2023   0   3
8 10.1080/13416979.2023.2257456 2024   0   0

4 The conceptual structure - Co-Word Analysis

Co-word networks show the conceptual structure, that uncovers links between concepts through term co-occurences.

Conceptual structure is often used to understand the topics covered by scholars (so-called research front) and identify what are the most important and the most recent issues.

Dividing the whole timespan in different timeslices and comparing the conceptual structures is useful to analyze the evolution of topics over time.

Bibliometrix is able to analyze keywords, but also the terms in the articles’ titles and abstracts. It does it using network analysis or correspondance analysis (CA) or multiple correspondance analysis (MCA). CA and MCA visualise the conceptual structure in a two-dimensional plot.

4.1 Co-word Analysis through Keyword co-occurrences

Plot options:

  • normalize = “association” (the vertex similarities are normalized using association strength)

  • n = 50 (the function plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of the vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 3 (defines the max size of vertex labels)

  • label.cex = TRUE (The vertex label sizes are proportional to their degree)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • label.n = 30 (Labels are plotted only for the main 30 vertices)

  • edges.min = 25 (plots only edges with a strength greater than or equal to 2)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=15, remove.multiple=F, edgesize = 10, labelsize=2,label.cex=TRUE,label.n=30,edges.min=2)

Descriptive analysis of keyword co-occurrences network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  385 
 Density                               0.028 
 Transitivity                          0.467 
 Diameter                              6 
 Degree Centralization                 0.194 
 Average path length                   3.024 
 

4.2 Co-word Analysis through Correspondence Analysis

suppressWarnings(
CS <- conceptualStructure(M, method="MCA", field="ID", stemming=FALSE, labelsize=15,documents=20)
)

5 Thematic Map

Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.

Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.

Please see:

Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M. (2022). Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy. Sustainability, 14(6), 3643, (https://doi.org/10.3390/su14063643).

Aria M., Misuraca M., Spano M. (2020) Mapping the evolution of social research and data science on 30 years of Social Indicators Research, Social Indicators Research. (DOI: )https://doi.org/10.1007/s11205-020-02281-3)

Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.

Map=thematicMap(M, field = "ID", n = 250, minfreq = 4,
  stemming = FALSE, size = 0.7, n.labels=5, repel = TRUE)
plot(Map$map)

Cluster description

Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 38 × 9
## # Groups:   Cluster_Label [13]
##    Occurrences Words        Cluster Color     Cluster_Label Cluster_Frequency btw_centrality clos_centrality pagerank_centrality
##          <dbl> <chr>          <dbl> <chr>     <chr>                     <dbl>          <dbl>           <dbl>               <dbl>
##  1           3 dynamics           1 #E41A1C80 dynamics                     13          1262.         0.00144             0.00823
##  2           2 policy             1 #E41A1C80 dynamics                     13           375.         0.00163             0.00579
##  3           2 quality            1 #E41A1C80 dynamics                     13           295.         0.00154             0.00422
##  4           2 scale              1 #E41A1C80 dynamics                     13           230.         0.00159             0.00479
##  5           2 system             1 #E41A1C80 dynamics                     13           195.         0.00144             0.00394
##  6           2 urbanization       1 #E41A1C80 dynamics                     13           263.         0.00147             0.00602
##  7          11 patterns           2 #377EB880 patterns                     89          4354.         0.00190             0.0232 
##  8           9 big data           2 #377EB880 patterns                     89          3641.         0.00206             0.0213 
##  9           8 conservation       2 #377EB880 patterns                     89          1989.         0.00181             0.0167 
## 10           6 biodiversity       2 #377EB880 patterns                     89          2336.         0.00190             0.0137 
## # … with 28 more rows

6 The social structure - Collaboration Analysis

Collaboration networks show how authors, institutions (e.g. universities or departments) and countries relate to others in a specific field of research. For example, the first figure below is a co-author network. It discovers regular study groups, hidden groups of scholars, and pivotal authors. The second figure is called “Edu collaboration network” and uncovers relevant institutions in a specific research field and their relations.

6.1 Author collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "authors", sep = ";")
net=networkPlot(NetMatrix,  n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=1)

Descriptive analysis of author collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  625 
 Density                               0.087 
 Transitivity                          0.999 
 Diameter                              8 
 Degree Centralization                 0.203 
 Average path length                   1.593 
 

6.2 Edu collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "universities", sep = ";")
net=networkPlot(NetMatrix,  n = 50, Title = "Edu collaboration",type = "auto", size=4,size.cex=F,edgesize = 3,labelsize=1)

Descriptive analysis of edu collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  340 
 Density                               0.136 
 Transitivity                          0.982 
 Diameter                              8 
 Degree Centralization                 0.306 
 Average path length                   2.508 
 

6.3 Country collaboration network

M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "countries", sep = ";")
net=networkPlot(NetMatrix,  n = dim(NetMatrix)[1], Title = "Country collaboration",type = "circle", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")

Descriptive analysis of country collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  57 
 Density                               0.61 
 Transitivity                          0.946 
 Diameter                              3 
 Degree Centralization                 0.283 
 Average path length                   1.408